At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

Hand, Foot, and Mouth Disease (HFMD) is an infectious disease that primarily affects young children. In densely populated Jiangsu Province in China, the impact of extreme meteorological factors on HFMD is a concern. We aimed to examine the association between extreme meteorological variables and HFMD infection risk using daily HFMD infections and meteorological data from 2010 to 2017 in Jiangsu Province. We used distributed lag non‐linear model (DLNM) to analyze the data, which can effectively capture the nuanced non‐linear dynamics and lag effects in the relationship between HFMD and extreme meteorological factors. Comparing the 10th and 90th percentiles of meteorological variables with their respective median values, our results showed that extremely low temperatures and high humidity were significantly associated with increased HFMD infection risk. The greatest effect of extremely low temperatures was observed at a lag of 1–2 days, elevating the risk by 18 ∼ 33% (RR = 1.18 ∼ 1.33). Extremely high humidity was found to increase the risk of infection, starting at a lag of 4 days. In contrast, extremely high temperatures, low humidity, and high wind speed were associated with reduced risk of infection at lag of 0–12 days, with the range of RR values being 0.60–0.98 for extremely high temperatures, 0.69–0.89 for extremely low humidity, and 0.84–0.98 for extremely high wind speed respectively. Our findings suggest that extreme meteorological factors can significantly impact the incidence of HFMD in Jiangsu Province, and highlight the need for effective public health protection measures during the periods of extreme meteorological condition, particularly for vulnerable populations.

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

With the ever-increasing occurrence of extreme weather events as a result of global climate change, the impact of extreme temperatures on human health has become a critical area of concern. Specifically, it is imperative to investigate the impact of extreme weather conditions on the health of residents. In this study, we analyze the daily death data from 13 prefecture-level cities in Jiangsu Province from January 2014 to September 2022, using the distributed lag nonlinear model (DLNM) to comprehensively account for factors such as relative humidity, atmospheric pressure, air pollutants, and other factors to evaluate the lag and cumulative effects of extreme low temperature and high temperature on the death of residents across different age groups. Additionally, we utilize the Geographical Detector to analyze the effects of various meteorological and environmental factors on the distribution of resident death in Jiangsu Province. This provides valuable insights that can guide health authorities in decision-making and in the protection of residents. The experimental results indicate that both extreme low and high temperatures increase the mortality of residents. We observe that the impact of extreme low temperatures has a delayed effect, peaking after 3–5 days and lasting up to 11–21 days. In contrast, the impact of extreme high temperature is greatest on the first day, and lasts only 2–4 days. Both extreme high and low temperatures increase the mortality of residents, with the former being more transient and stronger and the latter being more persistent and slower. Furthermore, residents over 75 years of age are more vulnerable to the effects of extreme temperatures. Finally, we note that the spatial distribution of resident deaths is most closely associated consistent with the spatial distribution of daily mean temperature, and there is significant spatial heterogeneity in deaths among residents in Jiangsu Province.

Respiratory diseases pose a serious threat to the health of residents in China and cause enormous socio‐economic losses. Jiangsu has a total of over 27 million vulnerable people in two groups: children under 15 years old and the elderly over 65 years old. Therefore, studies on the spatio‐temporal risks of respiratory diseases and their influencing factors in Jiangsu Province are of particular importance. Based on the Distributed Lag Nonlinear Model (DLNM) and the Geographically and Temporally Weighted Regression Model (GTWR), this study took the hospitalized cases of respiratory diseases in Jiangsu Province from 2020 to 2023 as research samples. It deeply explored the complex temporal relationships and spatial differences between environmental driving factors and the HRRD (hospitalization rate of respiratory diseases). It quantified the nonlinear relationships and lag effects of environmental driving factors on the HRRD. Additionally, it conducted a quantitative analysis of the specific impacts of each factor on the hospitalization rate under different spatio‐temporal conditions. The research results show that there are significant differences in the lag effects of extreme meteorological factors, air pollutants, and comprehensive meteorological factors on respiratory diseases among different age groups. Similarly, the impacts of these factors on the HRRD also exhibit different spatial heterogeneities. This research reveals the spatio‐temporal distribution patterns and driving mechanisms of respiratory diseases, offering a scientific foundation for the development of regionalized and differentiated prevention and control strategies.